Design of peptide analogues with improved activity using a novel de novo protein design approach

Recent advances in the treatment of the peptide design problem have led to the ability to select novel sequences given the structure of a peptide backbone. Despite these breakthroughs, issues related to the stability and functionality of these designed peptides remain sources of frustration. In this work, a novel method that addresses these issues for the computational design of peptides and proteins is introduced. The method is based on (i) in silico sequence selection for a template fold using a novel integer linear program (ILP) formulation and (ii) validation of fold stability and specificity through rigorous calculations of ensemble probabilities for the selected sequences. Through experimentally based functional analysis, the approach is shown to provide several peptide sequences with 6−7-fold improvement in activity over the synthetic therapeutic peptide compstatin, a 13-residue cyclic peptide that binds to complement component C3 and inhibits complement activation.

[1]  L. Loeb,et al.  Creating novel enzymes by applied molecular evolution. , 1997, Chemistry & biology.

[2]  Dimitrios Morikis,et al.  Solution structure of Compstatin, a potent complement inhibitor , 1998, Protein science : a publication of the Protein Society.

[3]  C. Pabo.  Molecular technology: Designing proteins and peptides , 1983, Nature.

[4]  C. Floudas.  Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications , 1995 .

[5]  W. DeGrado,et al.  Protein design, a minimalist approach. , 1989, Science.

[6]  S J Wodak,et al.  Automatic protein design with all atom force-fields by exact and heuristic optimization. , 2000, Journal of molecular biology.

[7]  Dimitrios Morikis,et al.  The Structural Basis of Compstatin Activity Examined by Structure-Function-based Design of Peptide Analogs and NMR* 210 , 2002, The Journal of Biological Chemistry.

[8]  John D Lambris,et al.  Structure and biology of complement protein C3, a connecting link between innate and acquired immunity , 2001, Immunological reviews.

[9]  M. Levitt,et al.  Energy functions that discriminate X-ray and near native folds from well-constructed decoys. , 1996, Journal of molecular biology.

[10]  J L Klepeis,et al.  Hybrid global optimization algorithms for protein structure prediction: alternating hybrids. , 2003, Biophysical journal.

[11]  Jay W. Ponder,et al.  Tertiary Templates for Proteins Use of Packing Criteria in the Enumeration of Allowed Different Structural Classes Sequences , 1987 .

[12]  Z. Luthey-Schulten,et al.  Ab initio protein structure prediction. , 2002, Current opinion in structural biology.

[13]  C. Adjiman,et al.  Global optimization of mixed‐integer nonlinear problems , 2000 .

[14]  J. Richardson,et al.  De novo design, expression, and characterization of Felix: a four-helix bundle protein of native-like sequence. , 1990, Science.

[15]  M. Levitt,et al.  De novo protein design. I. In search of stability and specificity. , 1999, Journal of molecular biology.

[16]  F. Richards,et al.  Construction of new ligand binding sites in proteins of known structure. I. Computer-aided modeling of sites with pre-defined geometry. , 1991, Journal of molecular biology.

[17]  N. Linial,et al.  On the design and analysis of protein folding potentials , 2000, Proteins.

[18]  J. Ponder,et al.  Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes. , 1987, Journal of molecular biology.

[19]  Dimitrios Morikis,et al.  Binding Kinetics, Structure-Activity Relationship, and Biotransformation of the Complement Inhibitor Compstatin1 , 2000, The Journal of Immunology.

[20]  David T. Jones,et al.  De novo protein design using pairwise potentials and a genetic algorithm , 1994, Protein science : a publication of the Protein Society.

[21]  S. L. Mayo,et al.  Automated design of the surface positions of protein helices , 1997, Protein science : a publication of the Protein Society.

[22]  Christodoulos A Floudas,et al.  Integrated computational and experimental approach for lead optimization and design of compstatin variants with improved activity. , 2003, Journal of the American Chemical Society.

[23]  John L. Klepeis,et al.  Ab initio Tertiary Structure Prediction of Proteins , 2003, J. Glob. Optim..

[24]  Johan Desmet,et al.  The dead-end elimination theorem and its use in protein side-chain positioning , 1992, Nature.

[25]  J L Klepeis,et al.  A new pairwise folding potential based on improved decoy generation and side‐chain packing , 2004, Proteins.

[26]  J Meller,et al.  Linear programming optimization and a double statistical filter for protein threading protocols , 2001, Proteins.

[27]  S L Mayo,et al.  Pairwise calculation of protein solvent-accessible surface areas. , 1998, Folding & design.

[28]  Drexler Ke,et al.  Molecular engineering: An approach to the development of general capabilities for molecular manipulation. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[29]  John L. Klepeis,et al.  Free energy calculations for peptides via deterministic global optimization , 1999 .

[30]  C. Adjiman,et al.  A global optimization method, αBB, for general twice-differentiable constrained NLPs—II. Implementation and computational results , 1998 .

[31]  John L. Klepeis,et al.  A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids , 2003 .

[32]  W. Lim,et al.  Deciphering the message in protein sequences: tolerance to amino acid substitutions. , 1990, Science.

[33]  Andrew M Wollacott,et al.  Prediction of amino acid sequence from structure , 2000, Protein science : a publication of the Protein Society.

[34]  K E Drexler,et al.  Molecular engineering: An approach to the development of general capabilities for molecular manipulation. , 1981, Proceedings of the National Academy of Sciences of the United States of America.

[35]  Warren P. Adams,et al.  A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems , 1998 .

[36]  Frances H. Arnold,et al.  Computational method to reduce the search space for directed protein evolution , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[37]  John L. Klepeis,et al.  Deterministic Global Optimization and Ab Initio Approaches for the Structure Prediction of Polypeptides, Dynamics of Protein Folding, and Protein‐Protein Interactions , 2002 .

[38]  J. L. Klepeis,et al.  Predicting peptide structures using NMR data and deterministic global optimization , 1999 .

[39]  J R Desjarlais,et al.  De novo design of the hydrophobic cores of proteins , 1995, Protein science : a publication of the Protein Society.

[40]  J R Desjarlais,et al.  Side-chain and backbone flexibility in protein core design. , 1999, Journal of molecular biology.

[41]  S. L. Mayo,et al.  Protein design automation , 1996, Protein science : a publication of the Protein Society.

[42]  S. L. Mayo,et al.  De novo protein design: fully automated sequence selection. , 1997, Science.

[43]  John D Lambris,et al.  Inhibition of human complement by a C3-binding peptide isolated from a phage-displayed random peptide library. , 1996, Journal of immunology.

[44]  Christodoulos A. Floudas,et al.  Deterministic global optimization - theory, methods and applications , 2010, Nonconvex optimization and its applications.

[45]  Frances H. Arnold,et al.  Directed evolution of a para-nitrobenzyl esterase for aqueous-organic solvents , 1996, Nature Biotechnology.

[46]  Christopher A. Voigt,et al.  Trading accuracy for speed: A quantitative comparison of search algorithms in protein sequence design. , 2000, Journal of molecular biology.

[47]  B Tidor,et al.  Altering dimerization specificity by changes in surface electrostatics , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[48]  R. Elber,et al.  Distance‐dependent, pair potential for protein folding: Results from linear optimization , 2000, Proteins.